Overview

Dataset statistics

Number of variables44
Number of observations217
Missing cells1874
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.8 KiB
Average record size in memory376.6 B

Variable types

Categorical22
Text6
DateTime4
Unsupported7
Numeric4
Boolean1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-18197/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
위생업태명 is highly imbalanced (67.3%)Imbalance
남성종사자수 is highly imbalanced (76.6%)Imbalance
여성종사자수 is highly imbalanced (73.5%)Imbalance
영업장주변구분명 is highly imbalanced (76.0%)Imbalance
등급구분명 is highly imbalanced (57.2%)Imbalance
총인원 is highly imbalanced (86.7%)Imbalance
보증액 is highly imbalanced (51.2%)Imbalance
월세액 is highly imbalanced (51.2%)Imbalance
시설총규모 is highly imbalanced (76.7%)Imbalance
인허가취소일자 has 217 (100.0%) missing valuesMissing
폐업일자 has 32 (14.7%) missing valuesMissing
휴업시작일자 has 217 (100.0%) missing valuesMissing
휴업종료일자 has 217 (100.0%) missing valuesMissing
재개업일자 has 217 (100.0%) missing valuesMissing
전화번호 has 64 (29.5%) missing valuesMissing
소재지면적 has 36 (16.6%) missing valuesMissing
소재지우편번호 has 13 (6.0%) missing valuesMissing
지번주소 has 13 (6.0%) missing valuesMissing
도로명주소 has 85 (39.2%) missing valuesMissing
도로명우편번호 has 87 (40.1%) missing valuesMissing
좌표정보(X) has 6 (2.8%) missing valuesMissing
좌표정보(Y) has 6 (2.8%) missing valuesMissing
다중이용업소여부 has 13 (6.0%) missing valuesMissing
전통업소지정번호 has 217 (100.0%) missing valuesMissing
전통업소주된음식 has 217 (100.0%) missing valuesMissing
홈페이지 has 217 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 05:35:19.315179
Analysis finished2024-05-11 05:35:20.578957
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3200000
217 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 217
100.0%

Length

2024-05-11T14:35:20.694731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:20.877364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 217
100.0%

관리번호
Text

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T14:35:21.193405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4774
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st row3200000-109-1899-00632
2nd row3200000-109-1994-00001
3rd row3200000-109-1994-00002
4th row3200000-109-1996-00004
5th row3200000-109-1996-00005
ValueCountFrequency (%)
3200000-109-1899-00632 1
 
0.5%
3200000-109-2013-00010 1
 
0.5%
3200000-109-2014-00013 1
 
0.5%
3200000-109-2012-00008 1
 
0.5%
3200000-109-2013-00001 1
 
0.5%
3200000-109-2013-00002 1
 
0.5%
3200000-109-2013-00003 1
 
0.5%
3200000-109-2013-00004 1
 
0.5%
3200000-109-2013-00005 1
 
0.5%
3200000-109-2013-00006 1
 
0.5%
Other values (207) 207
95.4%
2024-05-11T14:35:21.785820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2395
50.2%
- 651
 
13.6%
2 466
 
9.8%
1 444
 
9.3%
9 308
 
6.5%
3 275
 
5.8%
4 53
 
1.1%
5 50
 
1.0%
7 48
 
1.0%
6 47
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4123
86.4%
Dash Punctuation 651
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2395
58.1%
2 466
 
11.3%
1 444
 
10.8%
9 308
 
7.5%
3 275
 
6.7%
4 53
 
1.3%
5 50
 
1.2%
7 48
 
1.2%
6 47
 
1.1%
8 37
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2395
50.2%
- 651
 
13.6%
2 466
 
9.8%
1 444
 
9.3%
9 308
 
6.5%
3 275
 
5.8%
4 53
 
1.1%
5 50
 
1.0%
7 48
 
1.0%
6 47
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2395
50.2%
- 651
 
13.6%
2 466
 
9.8%
1 444
 
9.3%
9 308
 
6.5%
3 275
 
5.8%
4 53
 
1.1%
5 50
 
1.0%
7 48
 
1.0%
6 47
 
1.0%

인허가일자
Date

Distinct212
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum1994-02-18 00:00:00
Maximum2023-09-06 00:00:00
2024-05-11T14:35:22.043379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:22.325272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

영업상태코드
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3
185 
1
32 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 185
85.3%
1 32
 
14.7%

Length

2024-05-11T14:35:22.558721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:22.755532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 185
85.3%
1 32
 
14.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
폐업
185 
영업/정상
32 

Length

Max length5
Median length2
Mean length2.4423963
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row폐업
5th row영업/정상

Common Values

ValueCountFrequency (%)
폐업 185
85.3%
영업/정상 32
 
14.7%

Length

2024-05-11T14:35:22.966452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:23.149719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 185
85.3%
영업/정상 32
 
14.7%

상세영업상태코드
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2
185 
1
32 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 185
85.3%
1 32
 
14.7%

Length

2024-05-11T14:35:23.319346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:23.505380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 185
85.3%
1 32
 
14.7%

상세영업상태명
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
폐업
185 
영업
32 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row폐업
4th row폐업
5th row영업

Common Values

ValueCountFrequency (%)
폐업 185
85.3%
영업 32
 
14.7%

Length

2024-05-11T14:35:23.743919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:23.939586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 185
85.3%
영업 32
 
14.7%

폐업일자
Date

MISSING 

Distinct168
Distinct (%)90.8%
Missing32
Missing (%)14.7%
Memory size1.8 KiB
Minimum1999-03-13 00:00:00
Maximum2024-03-08 00:00:00
2024-05-11T14:35:24.137589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:24.385939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

전화번호
Text

MISSING 

Distinct138
Distinct (%)90.2%
Missing64
Missing (%)29.5%
Memory size1.8 KiB
2024-05-11T14:35:24.788590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.130719
Min length2

Characters and Unicode

Total characters1550
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)83.0%

Sample

1st row02 8735925
2nd row02 8754402
3rd row02 0
4th row02 8592668
5th row02
ValueCountFrequency (%)
02 122
39.1%
070 6
 
1.9%
8566022 4
 
1.3%
8754402 3
 
1.0%
889 3
 
1.0%
875 3
 
1.0%
8852270 2
 
0.6%
884 2
 
0.6%
873 2
 
0.6%
844 2
 
0.6%
Other values (154) 163
52.2%
2024-05-11T14:35:25.478743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 260
16.8%
2 232
15.0%
8 223
14.4%
189
12.2%
5 129
8.3%
3 108
7.0%
7 103
 
6.6%
6 93
 
6.0%
4 87
 
5.6%
1 72
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1361
87.8%
Space Separator 189
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260
19.1%
2 232
17.0%
8 223
16.4%
5 129
9.5%
3 108
7.9%
7 103
 
7.6%
6 93
 
6.8%
4 87
 
6.4%
1 72
 
5.3%
9 54
 
4.0%
Space Separator
ValueCountFrequency (%)
189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260
16.8%
2 232
15.0%
8 223
14.4%
189
12.2%
5 129
8.3%
3 108
7.0%
7 103
 
6.6%
6 93
 
6.0%
4 87
 
5.6%
1 72
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260
16.8%
2 232
15.0%
8 223
14.4%
189
12.2%
5 129
8.3%
3 108
7.0%
7 103
 
6.6%
6 93
 
6.0%
4 87
 
5.6%
1 72
 
4.6%

소재지면적
Real number (ℝ)

MISSING 

Distinct122
Distinct (%)67.4%
Missing36
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean39.597293
Minimum1.5
Maximum495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T14:35:25.731123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile4
Q19.9
median20
Q342.9
95-th percentile129
Maximum495
Range493.5
Interquartile range (IQR)33

Descriptive statistics

Standard deviation58.1723
Coefficient of variation (CV)1.4690979
Kurtosis24.588571
Mean39.597293
Median Absolute Deviation (MAD)13
Skewness4.1878942
Sum7167.11
Variance3384.0165
MonotonicityNot monotonic
2024-05-11T14:35:25.973925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 12
 
5.5%
10.0 7
 
3.2%
9.9 6
 
2.8%
3.3 5
 
2.3%
20.0 5
 
2.3%
16.5 4
 
1.8%
33.0 3
 
1.4%
30.0 3
 
1.4%
9.0 3
 
1.4%
5.0 3
 
1.4%
Other values (112) 130
59.9%
(Missing) 36
 
16.6%
ValueCountFrequency (%)
1.5 1
 
0.5%
2.0 1
 
0.5%
3.0 2
 
0.9%
3.3 5
2.3%
4.0 2
 
0.9%
4.5 1
 
0.5%
5.0 3
1.4%
5.58 1
 
0.5%
5.64 1
 
0.5%
6.0 2
 
0.9%
ValueCountFrequency (%)
495.0 1
0.5%
309.1 1
0.5%
298.48 1
0.5%
223.84 1
0.5%
176.96 1
0.5%
141.26 1
0.5%
132.0 1
0.5%
130.51 1
0.5%
130.17 1
0.5%
129.0 1
0.5%

소재지우편번호
Text

MISSING 

Distinct75
Distinct (%)36.8%
Missing13
Missing (%)6.0%
Memory size1.8 KiB
2024-05-11T14:35:26.415548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0343137
Min length6

Characters and Unicode

Total characters1231
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)15.7%

Sample

1st row151842
2nd row151869
3rd row151843
4th row151901
5th row151889
ValueCountFrequency (%)
151888 12
 
5.9%
151050 9
 
4.4%
151876 9
 
4.4%
151844 8
 
3.9%
151843 8
 
3.9%
151830 8
 
3.9%
151904 8
 
3.9%
151869 7
 
3.4%
151903 7
 
3.4%
151894 6
 
2.9%
Other values (65) 122
59.8%
2024-05-11T14:35:27.110896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 437
35.5%
5 243
19.7%
8 201
16.3%
0 84
 
6.8%
9 70
 
5.7%
4 54
 
4.4%
3 43
 
3.5%
7 39
 
3.2%
6 33
 
2.7%
2 20
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1224
99.4%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 437
35.7%
5 243
19.9%
8 201
16.4%
0 84
 
6.9%
9 70
 
5.7%
4 54
 
4.4%
3 43
 
3.5%
7 39
 
3.2%
6 33
 
2.7%
2 20
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1231
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 437
35.5%
5 243
19.7%
8 201
16.3%
0 84
 
6.8%
9 70
 
5.7%
4 54
 
4.4%
3 43
 
3.5%
7 39
 
3.2%
6 33
 
2.7%
2 20
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 437
35.5%
5 243
19.7%
8 201
16.3%
0 84
 
6.8%
9 70
 
5.7%
4 54
 
4.4%
3 43
 
3.5%
7 39
 
3.2%
6 33
 
2.7%
2 20
 
1.6%

지번주소
Text

MISSING 

Distinct182
Distinct (%)89.2%
Missing13
Missing (%)6.0%
Memory size1.8 KiB
2024-05-11T14:35:27.663231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length38
Mean length23.460784
Min length18

Characters and Unicode

Total characters4786
Distinct characters119
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)82.4%

Sample

1st row서울특별시 관악구 봉천동 1555-13
2nd row서울특별시 관악구 신림동 1587-35 외 2필지
3rd row서울특별시 관악구 봉천동 951-25
4th row서울특별시 관악구 신림동 1629-95 지하1층
5th row서울특별시 관악구 신림동 661-50
ValueCountFrequency (%)
서울특별시 204
22.1%
관악구 204
22.1%
신림동 112
12.2%
봉천동 87
 
9.4%
607-73 10
 
1.1%
지하1층 9
 
1.0%
1층 7
 
0.8%
729-22 6
 
0.7%
남현동 5
 
0.5%
541 5
 
0.5%
Other values (230) 272
29.5%
2024-05-11T14:35:28.426772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
902
18.8%
1 258
 
5.4%
동 212
 
4.4%
관 211
 
4.4%
악 210
 
4.4%
시 208
 
4.3%
서 204
 
4.3%
특 204
 
4.3%
별 204
 
4.3%
구 204
 
4.3%
Other values (109) 1969
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2573
53.8%
Decimal Number 1096
22.9%
Space Separator 902
 
18.8%
Dash Punctuation 189
 
3.9%
Other Punctuation 11
 
0.2%
Uppercase Letter 7
 
0.1%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 212
 
8.2%
관 211
 
8.2%
악 210
 
8.2%
시 208
 
8.1%
서 204
 
7.9%
특 204
 
7.9%
별 204
 
7.9%
구 204
 
7.9%
울 204
 
7.9%
신 120
 
4.7%
Other values (89) 592
23.0%
Decimal Number
ValueCountFrequency (%)
1 258
23.5%
6 142
13.0%
2 128
11.7%
0 101
 
9.2%
5 95
 
8.7%
7 80
 
7.3%
3 77
 
7.0%
8 74
 
6.8%
4 72
 
6.6%
9 69
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
S 2
28.6%
K 1
14.3%
G 1
14.3%
A 1
14.3%
Space Separator
ValueCountFrequency (%)
902
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2573
53.8%
Common 2206
46.1%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 212
 
8.2%
관 211
 
8.2%
악 210
 
8.2%
시 208
 
8.1%
서 204
 
7.9%
특 204
 
7.9%
별 204
 
7.9%
구 204
 
7.9%
울 204
 
7.9%
신 120
 
4.7%
Other values (89) 592
23.0%
Common
ValueCountFrequency (%)
902
40.9%
1 258
 
11.7%
- 189
 
8.6%
6 142
 
6.4%
2 128
 
5.8%
0 101
 
4.6%
5 95
 
4.3%
7 80
 
3.6%
3 77
 
3.5%
8 74
 
3.4%
Other values (5) 160
 
7.3%
Latin
ValueCountFrequency (%)
B 2
28.6%
S 2
28.6%
K 1
14.3%
G 1
14.3%
A 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2573
53.8%
ASCII 2213
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
902
40.8%
1 258
 
11.7%
- 189
 
8.5%
6 142
 
6.4%
2 128
 
5.8%
0 101
 
4.6%
5 95
 
4.3%
7 80
 
3.6%
3 77
 
3.5%
8 74
 
3.3%
Other values (10) 167
 
7.5%
Hangul
ValueCountFrequency (%)
동 212
 
8.2%
관 211
 
8.2%
악 210
 
8.2%
시 208
 
8.1%
서 204
 
7.9%
특 204
 
7.9%
별 204
 
7.9%
구 204
 
7.9%
울 204
 
7.9%
신 120
 
4.7%
Other values (89) 592
23.0%

도로명주소
Text

MISSING 

Distinct127
Distinct (%)96.2%
Missing85
Missing (%)39.2%
Memory size1.8 KiB
2024-05-11T14:35:28.942524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length40
Mean length30.484848
Min length22

Characters and Unicode

Total characters4024
Distinct characters132
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)93.9%

Sample

1st row서울특별시 관악구 관천로 17 (신림동,외 2필지)
2nd row서울특별시 관악구 난곡로 120 (신림동)
3rd row서울특별시 관악구 남부순환로161나길 46 (신림동)
4th row서울특별시 관악구 원신2길 34 (신림동, 신신림시장)
5th row서울특별시 관악구 난곡로 220 (신림동)
ValueCountFrequency (%)
서울특별시 132
 
16.6%
관악구 132
 
16.6%
신림동 67
 
8.4%
봉천동 48
 
6.1%
1층 21
 
2.6%
남부순환로 16
 
2.0%
지하1층 16
 
2.0%
난곡로 11
 
1.4%
27 8
 
1.0%
조원로16길 8
 
1.0%
Other values (237) 334
42.1%
2024-05-11T14:35:29.753854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
663
 
16.5%
1 163
 
4.1%
관 150
 
3.7%
악 145
 
3.6%
동 145
 
3.6%
시 136
 
3.4%
서 135
 
3.4%
( 133
 
3.3%
구 133
 
3.3%
) 133
 
3.3%
Other values (122) 2088
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2351
58.4%
Space Separator 663
 
16.5%
Decimal Number 616
 
15.3%
Open Punctuation 133
 
3.3%
Close Punctuation 133
 
3.3%
Other Punctuation 107
 
2.7%
Dash Punctuation 17
 
0.4%
Uppercase Letter 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 150
 
6.4%
악 145
 
6.2%
동 145
 
6.2%
시 136
 
5.8%
서 135
 
5.7%
구 133
 
5.7%
특 132
 
5.6%
별 132
 
5.6%
울 132
 
5.6%
로 114
 
4.8%
Other values (103) 997
42.4%
Decimal Number
ValueCountFrequency (%)
1 163
26.5%
2 112
18.2%
3 67
10.9%
0 63
 
10.2%
6 50
 
8.1%
4 48
 
7.8%
5 38
 
6.2%
7 28
 
4.5%
8 24
 
3.9%
9 23
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
S 1
33.3%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
663
100.0%
Open Punctuation
ValueCountFrequency (%)
( 133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2351
58.4%
Common 1669
41.5%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 150
 
6.4%
악 145
 
6.2%
동 145
 
6.2%
시 136
 
5.8%
서 135
 
5.7%
구 133
 
5.7%
특 132
 
5.6%
별 132
 
5.6%
울 132
 
5.6%
로 114
 
4.8%
Other values (103) 997
42.4%
Common
ValueCountFrequency (%)
663
39.7%
1 163
 
9.8%
( 133
 
8.0%
) 133
 
8.0%
2 112
 
6.7%
, 107
 
6.4%
3 67
 
4.0%
0 63
 
3.8%
6 50
 
3.0%
4 48
 
2.9%
Other values (5) 130
 
7.8%
Latin
ValueCountFrequency (%)
B 1
25.0%
S 1
25.0%
K 1
25.0%
a 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2351
58.4%
ASCII 1673
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
663
39.6%
1 163
 
9.7%
( 133
 
7.9%
) 133
 
7.9%
2 112
 
6.7%
, 107
 
6.4%
3 67
 
4.0%
0 63
 
3.8%
6 50
 
3.0%
4 48
 
2.9%
Other values (9) 134
 
8.0%
Hangul
ValueCountFrequency (%)
관 150
 
6.4%
악 145
 
6.2%
동 145
 
6.2%
시 136
 
5.8%
서 135
 
5.7%
구 133
 
5.7%
특 132
 
5.6%
별 132
 
5.6%
울 132
 
5.6%
로 114
 
4.8%
Other values (103) 997
42.4%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)55.4%
Missing87
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean8779.4077
Minimum8703
Maximum8863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T14:35:30.068928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8703
5-th percentile8715
Q18752.25
median8770
Q38805
95-th percentile8856.55
Maximum8863
Range160
Interquartile range (IQR)52.75

Descriptive statistics

Standard deviation42.610312
Coefficient of variation (CV)0.0048534381
Kurtosis-0.62656926
Mean8779.4077
Median Absolute Deviation (MAD)22.5
Skewness0.42322789
Sum1141323
Variance1815.6387
MonotonicityNot monotonic
2024-05-11T14:35:30.320034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8767 9
 
4.1%
8774 7
 
3.2%
8849 5
 
2.3%
8784 4
 
1.8%
8769 4
 
1.8%
8768 4
 
1.8%
8792 3
 
1.4%
8754 3
 
1.4%
8756 3
 
1.4%
8793 3
 
1.4%
Other values (62) 85
39.2%
(Missing) 87
40.1%
ValueCountFrequency (%)
8703 1
0.5%
8705 1
0.5%
8708 2
0.9%
8709 2
0.9%
8715 2
0.9%
8717 1
0.5%
8722 2
0.9%
8724 1
0.5%
8725 1
0.5%
8729 1
0.5%
ValueCountFrequency (%)
8863 2
 
0.9%
8861 1
 
0.5%
8860 2
 
0.9%
8857 2
 
0.9%
8856 2
 
0.9%
8854 1
 
0.5%
8849 5
2.3%
8848 2
 
0.9%
8846 2
 
0.9%
8840 1
 
0.5%

사업장명
Text

Distinct203
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T14:35:30.679202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length6.0414747
Min length2

Characters and Unicode

Total characters1311
Distinct characters302
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)87.1%

Sample

1st row동진물류
2nd row용기유통
3rd row(주)베스트스토아
4th row풍년식품
5th row난곡제과
ValueCountFrequency (%)
주식회사 6
 
2.5%
삼영식품 2
 
0.8%
풍전식품 2
 
0.8%
유로리더스 2
 
0.8%
굿스피드 2
 
0.8%
대우홈마트 2
 
0.8%
민속떡집 2
 
0.8%
관악농협농특산물백화점 2
 
0.8%
천지식품 2
 
0.8%
영우유통 2
 
0.8%
Other values (209) 215
90.0%
2024-05-11T14:35:31.696084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 46
 
3.5%
주 41
 
3.1%
마 40
 
3.1%
식 39
 
3.0%
) 39
 
3.0%
( 39
 
3.0%
품 28
 
2.1%
이 25
 
1.9%
22
 
1.7%
유 20
 
1.5%
Other values (292) 972
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1170
89.2%
Close Punctuation 39
 
3.0%
Open Punctuation 39
 
3.0%
Space Separator 22
 
1.7%
Lowercase Letter 19
 
1.4%
Uppercase Letter 15
 
1.1%
Decimal Number 6
 
0.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 46
 
3.9%
주 41
 
3.5%
마 40
 
3.4%
식 39
 
3.3%
품 28
 
2.4%
이 25
 
2.1%
유 20
 
1.7%
스 18
 
1.5%
드 17
 
1.5%
산 16
 
1.4%
Other values (259) 880
75.2%
Lowercase Letter
ValueCountFrequency (%)
e 3
15.8%
o 3
15.8%
l 2
10.5%
h 2
10.5%
s 1
 
5.3%
f 1
 
5.3%
r 1
 
5.3%
d 1
 
5.3%
t 1
 
5.3%
c 1
 
5.3%
Other values (3) 3
15.8%
Uppercase Letter
ValueCountFrequency (%)
O 2
13.3%
G 2
13.3%
K 1
 
6.7%
T 1
 
6.7%
R 1
 
6.7%
Z 1
 
6.7%
F 1
 
6.7%
C 1
 
6.7%
S 1
 
6.7%
Y 1
 
6.7%
Other values (3) 3
20.0%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
3 2
33.3%
5 2
33.3%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1169
89.2%
Common 107
 
8.2%
Latin 34
 
2.6%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 46
 
3.9%
주 41
 
3.5%
마 40
 
3.4%
식 39
 
3.3%
품 28
 
2.4%
이 25
 
2.1%
유 20
 
1.7%
스 18
 
1.5%
드 17
 
1.5%
산 16
 
1.4%
Other values (258) 879
75.2%
Latin
ValueCountFrequency (%)
e 3
 
8.8%
o 3
 
8.8%
O 2
 
5.9%
l 2
 
5.9%
G 2
 
5.9%
h 2
 
5.9%
s 1
 
2.9%
f 1
 
2.9%
r 1
 
2.9%
K 1
 
2.9%
Other values (16) 16
47.1%
Common
ValueCountFrequency (%)
) 39
36.4%
( 39
36.4%
22
20.6%
1 2
 
1.9%
3 2
 
1.9%
5 2
 
1.9%
& 1
 
0.9%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1169
89.2%
ASCII 141
 
10.8%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 46
 
3.9%
주 41
 
3.5%
마 40
 
3.4%
식 39
 
3.3%
품 28
 
2.4%
이 25
 
2.1%
유 20
 
1.7%
스 18
 
1.5%
드 17
 
1.5%
산 16
 
1.4%
Other values (258) 879
75.2%
ASCII
ValueCountFrequency (%)
) 39
27.7%
( 39
27.7%
22
15.6%
e 3
 
2.1%
o 3
 
2.1%
O 2
 
1.4%
l 2
 
1.4%
G 2
 
1.4%
h 2
 
1.4%
1 2
 
1.4%
Other values (23) 25
17.7%
CJK
ValueCountFrequency (%)
愛 1
100.0%

최종수정일자
Date

Distinct198
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum1999-12-17 00:00:00
Maximum2024-03-08 09:53:23
2024-05-11T14:35:31.962318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:32.300547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
I
174 
U
43 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 174
80.2%
U 43
 
19.8%

Length

2024-05-11T14:35:32.538048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:32.807412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 174
80.2%
u 43
 
19.8%

데이터갱신일자
Date

Distinct50
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-02 23:00:00
2024-05-11T14:35:33.013233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:33.304389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
식품소분업
217 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품소분업
2nd row식품소분업
3rd row식품소분업
4th row식품소분업
5th row식품소분업

Common Values

ValueCountFrequency (%)
식품소분업 217
100.0%

Length

2024-05-11T14:35:33.571033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:33.757226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 217
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct163
Distinct (%)77.3%
Missing6
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean193986.56
Minimum191076.43
Maximum198374.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T14:35:33.948961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191076.43
5-th percentile191517.87
Q1192674.89
median193792.63
Q3195062.39
95-th percentile196501.16
Maximum198374.47
Range7298.0445
Interquartile range (IQR)2387.5022

Descriptive statistics

Standard deviation1591.4006
Coefficient of variation (CV)0.0082036641
Kurtosis-0.43088544
Mean193986.56
Median Absolute Deviation (MAD)1117.7431
Skewness0.36089495
Sum40931164
Variance2532555.8
MonotonicityNot monotonic
2024-05-11T14:35:34.224987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192674.885947244 11
 
5.1%
192096.349629652 6
 
2.8%
193301.885808714 6
 
2.8%
194486.270488664 4
 
1.8%
194899.891834822 4
 
1.8%
193558.722989156 4
 
1.8%
192091.636556737 3
 
1.4%
193578.742465284 3
 
1.4%
192817.818457133 3
 
1.4%
196350.708293561 2
 
0.9%
Other values (153) 165
76.0%
(Missing) 6
 
2.8%
ValueCountFrequency (%)
191076.428759797 1
0.5%
191084.937997691 1
0.5%
191131.263415395 1
0.5%
191190.030869923 1
0.5%
191210.560973279 1
0.5%
191334.658955208 2
0.9%
191406.713826563 1
0.5%
191411.359729 1
0.5%
191505.279698921 2
0.9%
191530.450376231 1
0.5%
ValueCountFrequency (%)
198374.473281221 1
0.5%
198351.367148646 1
0.5%
197843.765038856 1
0.5%
197750.177024335 1
0.5%
197731.308064512 1
0.5%
196944.124634252 1
0.5%
196941.878774688 1
0.5%
196912.833572618 1
0.5%
196863.345683636 1
0.5%
196534.587783352 1
0.5%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct163
Distinct (%)77.3%
Missing6
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean442049.48
Minimum439817
Maximum443547.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-11T14:35:34.453247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439817
5-th percentile440786.43
Q1441572.72
median442151.62
Q3442532.44
95-th percentile443151.56
Maximum443547.05
Range3730.0505
Interquartile range (IQR)959.71558

Descriptive statistics

Standard deviation711.9886
Coefficient of variation (CV)0.0016106536
Kurtosis0.069028356
Mean442049.48
Median Absolute Deviation (MAD)450.7827
Skewness-0.44838193
Sum93272440
Variance506927.76
MonotonicityNot monotonic
2024-05-11T14:35:34.696463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441352.743493574 11
 
5.1%
442339.213626523 6
 
2.8%
443151.561302292 6
 
2.8%
442632.469540589 4
 
1.8%
442135.938022991 4
 
1.8%
442164.620829978 4
 
1.8%
442282.244935168 3
 
1.4%
443547.049696825 3
 
1.4%
440263.768319604 3
 
1.4%
442532.440501316 2
 
0.9%
Other values (153) 165
76.0%
(Missing) 6
 
2.8%
ValueCountFrequency (%)
439816.999224208 1
 
0.5%
440263.768319604 3
1.4%
440293.901638173 1
 
0.5%
440414.802507964 1
 
0.5%
440498.447701298 1
 
0.5%
440699.010268516 1
 
0.5%
440704.28521114 1
 
0.5%
440755.399762788 1
 
0.5%
440760.03645246 1
 
0.5%
440812.826237103 1
 
0.5%
ValueCountFrequency (%)
443547.049696825 3
1.4%
443341.379446435 1
 
0.5%
443287.141450241 1
 
0.5%
443273.487218185 1
 
0.5%
443241.362831726 1
 
0.5%
443151.561302292 6
2.8%
443148.827256697 1
 
0.5%
443130.607614887 1
 
0.5%
443124.728845298 1
 
0.5%
443067.134957717 1
 
0.5%

위생업태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
식품소분업
204 
<NA>
 
13

Length

Max length5
Median length5
Mean length4.9400922
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품소분업
2nd row식품소분업
3rd row식품소분업
4th row식품소분업
5th row식품소분업

Common Values

ValueCountFrequency (%)
식품소분업 204
94.0%
<NA> 13
 
6.0%

Length

2024-05-11T14:35:34.937474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:35.121041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 204
94.0%
na 13
 
6.0%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
201 
0
 
10
1
 
5
4
 
1

Length

Max length4
Median length4
Mean length3.7788018
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 201
92.6%
0 10
 
4.6%
1 5
 
2.3%
4 1
 
0.5%

Length

2024-05-11T14:35:35.321242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:35.521453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 201
92.6%
0 10
 
4.6%
1 5
 
2.3%
4 1
 
0.5%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
201 
0
 
14
1
 
2

Length

Max length4
Median length4
Mean length3.7788018
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 201
92.6%
0 14
 
6.5%
1 2
 
0.9%

Length

2024-05-11T14:35:35.729337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:35.922494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 201
92.6%
0 14
 
6.5%
1 2
 
0.9%

영업장주변구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
198 
기타
 
10
주택가주변
 
7
학교정화(상대)
 
1
아파트지역
 
1

Length

Max length8
Median length4
Mean length3.9631336
Min length2

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row주택가주변
2nd row<NA>
3rd row<NA>
4th row주택가주변
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 198
91.2%
기타 10
 
4.6%
주택가주변 7
 
3.2%
학교정화(상대) 1
 
0.5%
아파트지역 1
 
0.5%

Length

2024-05-11T14:35:36.148744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:36.331019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
91.2%
기타 10
 
4.6%
주택가주변 7
 
3.2%
학교정화(상대 1
 
0.5%
아파트지역 1
 
0.5%

등급구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
198 
기타
 
19

Length

Max length4
Median length4
Mean length3.8248848
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row<NA>
3rd row<NA>
4th row기타
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 198
91.2%
기타 19
 
8.8%

Length

2024-05-11T14:35:36.528056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:36.718873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
91.2%
기타 19
 
8.8%

급수시설구분명
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
189 
상수도전용
28 

Length

Max length5
Median length4
Mean length4.1290323
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row상수도전용
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 189
87.1%
상수도전용 28
 
12.9%

Length

2024-05-11T14:35:36.887475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:37.052749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 189
87.1%
상수도전용 28
 
12.9%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
213 
0
 
4

Length

Max length4
Median length4
Mean length3.9447005
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 213
98.2%
0 4
 
1.8%

Length

2024-05-11T14:35:37.243722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:37.430189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 213
98.2%
0 4
 
1.8%

본사종업원수
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
148 
0
69 

Length

Max length4
Median length4
Mean length3.0460829
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 148
68.2%
0 69
31.8%

Length

2024-05-11T14:35:37.657726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:37.869937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
68.2%
0 69
31.8%

공장사무직종업원수
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
148 
0
69 

Length

Max length4
Median length4
Mean length3.0460829
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 148
68.2%
0 69
31.8%

Length

2024-05-11T14:35:38.121277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:38.377388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
68.2%
0 69
31.8%

공장판매직종업원수
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
148 
0
69 

Length

Max length4
Median length4
Mean length3.0460829
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 148
68.2%
0 69
31.8%

Length

2024-05-11T14:35:38.559288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:38.757468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
68.2%
0 69
31.8%

공장생산직종업원수
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
148 
0
69 

Length

Max length4
Median length4
Mean length3.0460829
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 148
68.2%
0 69
31.8%

Length

2024-05-11T14:35:38.967701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:39.143002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 148
68.2%
0 69
31.8%

건물소유구분명
Categorical

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
83 
임대
79 
자가
55 

Length

Max length4
Median length2
Mean length2.764977
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 83
38.2%
임대 79
36.4%
자가 55
25.3%

Length

2024-05-11T14:35:39.298367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:39.457974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
38.2%
임대 79
36.4%
자가 55
25.3%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
194 
0
23 

Length

Max length4
Median length4
Mean length3.6820276
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 194
89.4%
0 23
 
10.6%

Length

2024-05-11T14:35:39.650219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:39.812163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 194
89.4%
0 23
 
10.6%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
194 
0
23 

Length

Max length4
Median length4
Mean length3.6820276
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 194
89.4%
0 23
 
10.6%

Length

2024-05-11T14:35:39.955433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:40.110212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 194
89.4%
0 23
 
10.6%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing13
Missing (%)6.0%
Memory size566.0 B
False
204 
(Missing)
 
13
ValueCountFrequency (%)
False 204
94.0%
(Missing) 13
 
6.0%
2024-05-11T14:35:40.247184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
203 
<NA>
 
13
33
 
1

Length

Max length4
Median length1
Mean length1.1843318
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 203
93.5%
<NA> 13
 
6.0%
33 1
 
0.5%

Length

2024-05-11T14:35:40.381751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:40.563548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 203
93.5%
na 13
 
6.0%
33 1
 
0.5%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing217
Missing (%)100.0%
Memory size2.0 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
032000003200000-109-1899-0063219991105<NA>3폐업2폐업20031112<NA><NA><NA>02 873592515.0151842서울특별시 관악구 봉천동 1555-13<NA><NA>동진물류2001-11-30 00:00:00I2018-08-31 23:59:59.0식품소분업194919.194408441542.753266식품소분업10주택가주변기타<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
132000003200000-109-1994-0000119940427<NA>3폐업2폐업20141016<NA><NA><NA><NA><NA>151869서울특별시 관악구 신림동 1587-35 외 2필지서울특별시 관악구 관천로 17 (신림동,외 2필지)8774용기유통2006-11-21 00:00:00I2018-08-31 23:59:59.0식품소분업193590.018315442173.666976식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0<NA><NA><NA>
232000003200000-109-1994-0000219941128<NA>3폐업2폐업20130808<NA><NA><NA>02 875440224.0151843서울특별시 관악구 봉천동 951-25<NA><NA>(주)베스트스토아2006-01-11 00:00:00I2018-08-31 23:59:59.0식품소분업194486.270489442632.469541식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0<NA><NA><NA>
332000003200000-109-1996-0000419960521<NA>3폐업2폐업20030404<NA><NA><NA>02 062.68151901서울특별시 관악구 신림동 1629-95 지하1층<NA><NA>풍년식품2001-11-30 00:00:00I2018-08-31 23:59:59.0식품소분업193595.495978441787.184978식품소분업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
432000003200000-109-1996-0000519960827<NA>1영업/정상1영업<NA><NA><NA><NA>02 8592668<NA>151889서울특별시 관악구 신림동 661-50서울특별시 관악구 난곡로 120 (신림동)8857난곡제과2001-10-18 00:00:00I2018-08-31 23:59:59.0식품소분업192875.838986440414.802508식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0<NA><NA><NA>
532000003200000-109-1997-0000519970325<NA>3폐업2폐업20000327<NA><NA><NA>0214.93151876서울특별시 관악구 신림동 539-1<NA><NA>팽귄식품2000-03-27 00:00:00I2018-08-31 23:59:59.0식품소분업192158.132223442317.118365식품소분업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
632000003200000-109-1997-0000619970425<NA>3폐업2폐업20011214<NA><NA><NA>02 884133816.5151890서울특별시 관악구 신림동 1413-36<NA><NA>울릉도특산1999-12-17 00:00:00I2018-08-31 23:59:59.0식품소분업194019.859938442629.433829식품소분업00주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
732000003200000-109-1997-0000719970704<NA>3폐업2폐업20020115<NA><NA><NA>02 598515724.42151802서울특별시 관악구 남현동 1068-11<NA><NA>남원폐백2001-09-29 00:00:00I2018-08-31 23:59:59.0식품소분업197750.177024441488.469007식품소분업<NA><NA>주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
832000003200000-109-1997-0000819971030<NA>3폐업2폐업20070716<NA><NA><NA>023289801013.6151830서울특별시 관악구 봉천동 729-22<NA><NA>롯데쇼핑(주)롯데마트관악점2005-01-04 00:00:00I2018-08-31 23:59:59.0식품소분업193301.885809443151.561302식품소분업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0<NA><NA><NA>
932000003200000-109-1997-0000919970319<NA>3폐업2폐업20110816<NA><NA><NA>02 8834322<NA>151895서울특별시 관악구 신림동 1523<NA><NA>원신산업(미림점)2003-11-28 00:00:00I2018-08-31 23:59:59.0식품소분업<NA><NA>식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
20732000003200000-109-2020-0000220200402<NA>3폐업2폐업20221018<NA><NA><NA>02 886 098212.0151834서울특별시 관악구 봉천동 1666-32 샤론서울특별시 관악구 남부순환로 1837, 샤론빌딩 402호 (봉천동)8738아이셀소프트2022-10-18 10:29:35U2021-10-30 22:00:00.0식품소분업195903.942549442081.454852<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20832000003200000-109-2021-000012021-03-04<NA>3폐업2폐업2023-12-01<NA><NA><NA><NA>6.0151-810서울특별시 관악구 봉천동 33-3서울특별시 관악구 관악로 222, 3층 305호 (봉천동)8737이앤제이비2023-12-01 17:54:08U2022-11-02 00:03:00.0식품소분업195999.282149442463.795943<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20932000003200000-109-2021-0000220210823<NA>1영업/정상1영업<NA><NA><NA><NA><NA>59.58151889서울특별시 관악구 신림동 701-1서울특별시 관악구 난곡로 158, 4층 (신림동)8857당플2021-08-23 15:23:31I2021-08-25 00:22:50.0식품소분업192875.641473440760.036452식품소분업00<NA><NA><NA>00000자가00N0<NA><NA><NA>
21032000003200000-109-2022-000012022-05-25<NA>3폐업2폐업2024-02-13<NA><NA><NA><NA>115.7151-913서울특별시 관악구 봉천동 952-20 정암빌딩서울특별시 관악구 은천로 25, 정암빌딩 1층 104호 (봉천동)8717오케이농산 (OK농산)2024-02-13 13:37:10U2023-12-01 23:05:00.0식품소분업194470.351057442697.441464<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21132000003200000-109-2022-0000220220624<NA>1영업/정상1영업<NA><NA><NA><NA><NA>13.2151801서울특별시 관악구 남현동 612-51서울특별시 관악구 과천대로 909, 지하 1층 (남현동)8808툴레도스(Toledoth)2022-06-24 09:33:23I2021-12-05 22:06:00.0식품소분업198374.473281441033.513045<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21232000003200000-109-2022-000032022-11-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.96151-888서울특별시 관악구 신림동 607-118서울특별시 관악구 난곡로 230, 1층 1호 (신림동)8849주전부리 집합소2023-07-20 16:35:42U2022-12-06 22:02:00.0식품소분업192596.181225441403.834375<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21332000003200000-109-2023-000012023-02-07<NA>1영업/정상1영업<NA><NA><NA><NA><NA>38.51151-880서울특별시 관악구 신림동 626-5서울특별시 관악구 난곡로34길 43, 지하1층 (신림동)8854하얀비식품2023-02-07 16:44:42I2022-12-02 00:09:00.0식품소분업192983.780353441111.86137<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21432000003200000-109-2023-000022023-07-25<NA>3폐업2폐업2024-03-08<NA><NA><NA><NA>3.3151-809서울특별시 관악구 봉천동 30-3 호삼빌딩서울특별시 관악구 관악로24길 14, 호삼빌딩 5층 32호 (봉천동)8737로지2024-03-08 09:53:23U2023-12-02 23:00:00.0식품소분업195999.663233442420.839864<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21532000003200000-109-2023-000032023-08-22<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.0151-890서울특별시 관악구 신림동 1416-22서울특별시 관악구 신림로70길 27, 1층 102호 (신림동)8753과일담은요거트맛집 요맛신림점2023-08-22 14:51:41I2022-12-07 22:04:00.0식품소분업193777.026154442835.333111<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21632000003200000-109-2023-000042023-09-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>4.0151-869서울특별시 관악구 신림동 1578-51 관악정보통신빌딩서울특별시 관악구 남부순환로 1568, 관악정보통신빌딩 5층 8호 (신림동)8773리네뜨2023-09-06 15:59:12I2022-12-09 00:08:00.0식품소분업193288.31084442356.271957<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>